Solving Imbalanced Problem of Muticlass Data Set with Class Balancer and Synthetic Minority Over-sampling Technique

Abstract

Classifying multiclass data set frequently
leads to poor results. Therefore, this research tends to
solve imbalanced multiclass data set. We compare the
data undergone class imbalance problem solving process
with the unsolved data to look for predictive modelling
most suitable for imbalanced multiclass data set. As it
contains multiple classes, we treat each class equitably.
Dispersion of accurate prediction for each class is of
result consideration.